Compute Degree Days
degday provides formulas for estimating degree days from daily minimum and maximum temperatures. Degree days are commonly used in agriculture to predict the growth of plants and insects. To use the formulas, you must pass a record of daily minimum and maximum temperatures, as well as provide a species-specific temperature range in which development occurs.
Formulas are based on the work by Zalom et al (1983). These same formulas are used on the UC Integrated Pest Management (UC IPM) website. See the UC IPM website for additional background on degree days, detailed figures and formulas, and models that predict growth stages of various crops and pests based on degree days.
degday implements all the formulas in Zalom et al (1983), including the single and double triangle methods, the single and double sine methods, and the simple average method. All formulas use the horizontal cutoff method. degday does not currently support the vertical cutoff method, nor the corrected sine and triangle methods.
You might be wondering which of the four estimation methods to use? Most people compute degree days not as an end in itself, but rather to look-up when a development milestone, such as blooming in a plant or larvae emergence in an insect, is expected. Hence you should use whichever method of degree days referenced in the lookup table. When in doubt, use the single-sine method. For more info about the different methods, see Roltsch et al (1999).
Degree days are sometimes referred to as growing degree days, which generally refers to data that drives models of specifically plant growth. Other terms that are more or less synonymous with degree days are heat units and thermal units. Chill hours is a related concept, but uses accumulated cold to predict plant and insect development rather than accumulated heat. Formulas for computing chill hours and chill portions may be found in chillR.
Installation
To install the development version of degday, run the following. Windows users need to have RTools installed first.
remotes::install_github("ucanr-igis/degday")Example
To illustrate, we can compute degree days for a sample dataset consisting of one year of minimum and maximum daily temperatures from the Esparto.A CIMIS weather station in Yolo County, California.
library(degday)
library(dplyr)
espartoa_csv <- system.file("extdata/espartoa-weather-2020.csv", package = "degday")
espartoa_temp <- read.csv(espartoa_csv) %>% mutate(date = as.Date(date))
espartoa_temp %>% head()
#> station date tmin tmax
#> 1 Esparto.A 2020-01-01 38 55
#> 2 Esparto.A 2020-01-02 36 67
#> 3 Esparto.A 2020-01-03 33 59
#> 4 Esparto.A 2020-01-04 37 59
#> 5 Esparto.A 2020-01-05 38 63
#> 6 Esparto.A 2020-01-06 36 58To compute degree days, we have to tell it a range of temperatures in which development occurs. We’ll select 55.0°F and 93.9°F, which are the lower and upper thresholds for Navel Orangeworm.
thresh_low <- 55
thresh_up <- 93.9The single-triangle and single-sine methods can be computed with
dd_sng_tri() and dd_sng_sine(). We can add them as columns in our
table using mutate():
espartoa_dd <- espartoa_temp %>%
mutate(sng_tri = dd_sng_tri(daily_min = tmin, daily_max = tmax,
thresh_low = thresh_low, thresh_up = thresh_up),
sng_sine = dd_sng_sine(daily_min = tmin, daily_max = tmax,
thresh_low = thresh_low, thresh_up = thresh_up))
#> - using single triangle method
#> - using single sine method
espartoa_dd %>% head()
#> station date tmin tmax sng_tri sng_sine
#> 1 Esparto.A 2020-01-01 38 55 0.0000000 0.0000000
#> 2 Esparto.A 2020-01-02 36 67 2.3225806 3.3101298
#> 3 Esparto.A 2020-01-03 33 59 0.3076923 0.6766645
#> 4 Esparto.A 2020-01-04 37 59 0.3636364 0.7378844
#> 5 Esparto.A 2020-01-05 38 63 1.2800000 1.9896042
#> 6 Esparto.A 2020-01-06 36 58 0.2045455 0.4768866To compute degree days using the double-triangle and double-sine methods, we need to first add a column to our temperature table for the “day after” minimum temperature. That’s because these methods use the minimum temperature of the next day to better model cooling in the afternoon and evening hours.
We can add the next-day minimum temperature to our table with a little dplyr. Note this requires us to drop-the final row (because we don’t have a next-day temperature for it).
espartoa_temp2 <- espartoa_temp %>%
mutate(tmin_next = lead(tmin, n = 1)) %>%
slice(-n())
espartoa_temp2 %>% head()
#> station date tmin tmax tmin_next
#> 1 Esparto.A 2020-01-01 38 55 36
#> 2 Esparto.A 2020-01-02 36 67 33
#> 3 Esparto.A 2020-01-03 33 59 37
#> 4 Esparto.A 2020-01-04 37 59 38
#> 5 Esparto.A 2020-01-05 38 63 36
#> 6 Esparto.A 2020-01-06 36 58 30The double-triangle and double-sine methods can be computed with
dd_dbl_tri() and dd_dbl_sine().
espartoa_dd2 <- espartoa_temp2 %>%
mutate(dbl_tri = dd_dbl_tri(daily_min = tmin, daily_max = tmax, nextday_min = tmin_next,
thresh_low = thresh_low, thresh_up = thresh_up),
dbl_sine = dd_dbl_sine(daily_min = tmin, daily_max = tmax, nextday_min = tmin_next,
thresh_low = thresh_low, thresh_up = thresh_up))
#> - using double triangle method
#> - using double sine method
espartoa_dd2 %>% head()
#> station date tmin tmax tmin_next dbl_tri dbl_sine
#> 1 Esparto.A 2020-01-01 38 55 36 0.0000000 0.0000000
#> 2 Esparto.A 2020-01-02 36 67 33 2.2201139 3.2285910
#> 3 Esparto.A 2020-01-03 33 59 37 0.3356643 0.7072744
#> 4 Esparto.A 2020-01-04 37 59 38 0.3722944 0.7469314
#> 5 Esparto.A 2020-01-05 38 63 36 1.2325926 1.9493055
#> 6 Esparto.A 2020-01-06 36 58 30 0.1826299 0.4491396References
Zalom, F.G., P.B. Goodell, L.T. Wilson, W.W. Barnett, and W.J. Bentley. 1983. Degree-days: The calculation and use of heat units in pest management. UC DANR Leaflet 21373. Available from Hathi Trust.
Roltsch, W. J.; Zalom, F. G.; Strawn, A. J.; Strand, J. F.; Pitcairn, M. J. 1999. Evaluation of several degree-day estimation methods in California climates. Int. J. Biometeorol. 42:169-176. https://doi.org/10.1007/s004840050101